⚡ electrical engineering

Quantifying Realizable Flexibility Limits in Fast and Ultra-Fast EV Charging Using Real-World Data

This paper presents a data-driven framework using 252 real-world charging sessions to quantify the intrinsic, time-dependent flexibility limits of fast and ultra-fast EV charging, revealing that actual capabilities are strictly bounded by battery management system constraints rather than simple power controllability.

Original authors: Cesar Diaz-Londono, Liu Zhang, Jorge De La Cruz, Hamidreza Arasteh, Anand R., Daogui Tang, Josep M. Guerrero

Published 2026-06-30

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Cesar Diaz-Londono, Liu Zhang, Jorge De La Cruz, Hamidreza Arasteh, Anand R., Daogui Tang, Josep M. Guerrero

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you have a fleet of electric vehicles (EVs) plugged into fast chargers at a gas station. The power grid operator wants to know: "How much can we ask these cars to change their charging behavior to help the grid?" Maybe they want the cars to charge a little slower to save power, or even discharge a little energy back to the grid during a peak hour.

This paper says: "Stop guessing. Let's look at the actual data to see what is physically possible."

Here is the breakdown of their findings using simple analogies:

1. The "Driver" vs. The "Car" (Who is in Control?)

Most people think the charging station (the "plug") is in charge. They imagine the grid operator can just tell the station, "Hey, turn the power down by 50% right now!"

The Paper's Reality: The car is actually the boss.
Think of the car's battery management system (BMS) as a strict personal trainer. The trainer knows exactly how much the battery can handle at any given moment.

If the battery is low, the trainer says, "Go hard! Charge at full speed!"
If the battery is nearly full, the trainer says, "Slow down! We can't push too hard or we'll hurt the battery."
The Lesson: You can't just force the car to change its speed arbitrarily. The car has a pre-set "training plan" (a Power-SoC curve) that it must follow to stay safe. The flexibility isn't about turning a dial; it's about shifting the timing of that training plan.

2. The "Waiting Room" (Where Flexibility Lives)

The paper analyzes 252 real charging sessions from 52 different car brands. They found that flexibility only exists in the "waiting room."

The Mandatory Part: When you plug in, the car must charge from, say, 20% to 80% as fast as it can. You can't ask it to slow down here without delaying your trip.
The Idle Time: Flexibility only happens if you stay plugged in after you've reached your target charge.
- Analogy: Imagine you finish your workout at 5:00 PM, but you don't leave the gym until 6:00 PM. That extra hour is your "flexibility window."
- The paper shows that if you have this extra time, you can shift when you drink your water (energy) during the workout, but you can't change the workout itself.

3. The "Sliding Window" (How Much Can We Move?)

The researchers used a "sliding window" concept to measure flexibility.

Imagine you have a 10-minute window of extra time.
Question: How much energy can we move around inside that 10 minutes?
Answer: It depends on the car. Some cars can shift a lot of energy; others can only shift a tiny bit.
The Finding: The more extra time you have (the longer the idle period), the more energy you can shift. But it's not infinite. It's bounded by the car's battery size and its specific "training plan."

4. The "Two-Way Street" (Charging and Discharging)

The paper also looked at Bidirectional Flexibility (Vehicle-to-Grid), where the car gives energy back to the grid.

The Analogy: Imagine you are at the gym. You have 10 extra minutes. Can you do a quick set of push-ups (discharge) and then finish your workout (recharge) before you leave?
The Catch: You can only do this if:
1. You have enough time to do the push-ups and get back to your target state before leaving.
2. Your trainer (BMS) agrees it's safe to push that hard.
The Result: The paper found that the amount of energy a car can give back is strictly limited. Sometimes, depending on when you start the maneuver, a car can give back twice as much energy as you might expect, but only if the timing is perfect. If you try to push too deep or too long, the "trainer" shuts it down to protect the battery.

5. The "Real-World" vs. "Theoretical" Gap

Many previous studies assumed EVs were like simple buckets that could be filled or emptied at any speed the grid wanted.

The Paper's Correction: EVs are not simple buckets. They are complex, smart devices with strict safety rules.
The Takeaway: You cannot treat EV flexibility as a "controllable power switch." Instead, it is a "bounded energy resource."
- It is State-Dependent: It depends on how full the battery is.
- It is Time-Dependent: It depends on how long the car is parked.
- It is Brand-Dependent: A Tesla behaves differently than a Ford or a Hyundai.

Summary

The paper tells us that to use electric cars to help the power grid, we need to stop thinking, "How much power can we turn on or off?" and start thinking, "How much energy can we shift in time, given the car's specific rules and how long it's parked?"

The flexibility is real, but it is bounded, predictable, and strictly limited by the car's own safety systems and the user's schedule. It's not a magic wand; it's a carefully calculated opportunity.

Technical Summary: Quantifying Realizable Flexibility Limits in Fast and Ultra-Fast EV Charging Using Real-World Data

1. Problem Statement

The rapid electrification of transportation has positioned Electric Vehicles (EVs) as critical distributed energy resources capable of supporting grid stability, renewable integration, and congestion mitigation. However, existing literature often models EV flexibility as a freely dispatchable power resource, relying on simplified assumptions such as constant-power charging or arbitrary temporal shifts. These models fail to account for the intrinsic physical constraints of fast and ultra-fast DC charging, where the charging trajectory is tightly governed by the vehicle's Battery Management System (BMS), battery chemistry, and protection limits.

The core problem addressed is the lack of a realistic framework to quantify the physically achievable flexibility of fast and ultra-fast charging sessions. Current approaches overlook the non-linear, state-dependent nature of Power–State of Charge (SoC) relationships and the critical role of idle time availability. Consequently, system operators and aggregators may overestimate the controllability of EV fleets, leading to infeasible scheduling and potential battery degradation.

2. Methodology

The paper proposes a data-driven, trajectory-aware framework to quantify EV flexibility as an energy-bounded and time-constrained process. The methodology is built upon the following pillars:

Real-World Dataset: The study utilizes 252 real-world DC charging sessions from Fastned, covering 52 EV brands and 141 distinct Power–SoC behaviors. These profiles span battery capacities from 18.7 kWh to 111.2 kWh and peak charging powers from 35 kW to 403 kW.
Profile Reconstruction: To ensure consistency, incomplete raw telemetry data is reconstructed into complete 1–100% SoC trajectories. Linear extrapolation is applied at boundaries with a minimum power floor to preserve trends without artificial assumptions.
Synthetic Discharge Modeling: Given the scarcity of real-world Vehicle-to-Grid (V2G) data, synthetic discharge profiles are generated. These are derived from measured charging curves using a voltage-limited battery model. The method employs mirror mapping around a central SoC (50%), amplitude scaling, soft peak-power capping (90th percentile), and ramp-rate limiting to create physically plausible, conservative discharge envelopes.
Flexibility Formulation:
- Unidirectional Flexibility: Defined as the maximum shiftable charging energy within a specific time window ( $\Delta t$ ) while maintaining the final target SoC. A sliding-window analysis identifies the upper and lower bounds of energy that can be temporally redistributed.
- Bidirectional Flexibility: Defined as the maximum extractable discharge energy followed by a recharge to the target SoC. This is constrained by:
  1. Time Feasibility: The discharge and subsequent recharge must fit within the available idle time.
  2. Battery Protection: Constraints include a minimum SoC limit ( $SoC_{prot}$ ) and a maximum Depth of Discharge ( $\Delta SoC_{max} = 60\%$ ) per session to prevent excessive degradation.
Fleet-Level Aggregation: The framework aggregates single-EV results to generate fleet-level flexibility bounds, feasibility maps, and statistical indicators (e.g., feasibility rates, normalized flexibility).

3. Key Contributions

The paper makes four primary contributions to the state of the art:

Comprehensive BMS-Aware Dataset: The creation of a unified dataset of 141 distinct Power–SoC trajectories derived from 252 real fast/ultra-fast charging sessions, categorized into three groups based on charging capability (Standard Fast, High-Power Fast, and Ultra-Fast).
Systematic Performance Evaluation: A systematic analysis of charging performance across various charger ratings (7–480 kW), quantifying the diminishing returns of higher charger power due to BMS saturation, particularly in the 80–100% SoC range.
Data-Driven Intra-Session Flexibility Metrics: The introduction of sliding-window metrics to estimate shiftable energy and normalized SoC flexibility, providing an empirical assessment of flexibility potential without relying on arbitrary power-control assumptions.
Bounded Bidirectional Flexibility Framework: A novel method to estimate bidirectional flexibility limits using only charging data. By combining scaled symmetry-based discharge envelopes with window-based energy metrics, the study provides realistic bounds for extractable energy under idle-time and battery-protection constraints.

4. Key Results

Charging Dynamics: Charging time in the 20%–80% SoC range drops by over 60% compared to the full cycle, and mean power increases by up to 40%. However, charging beyond 80% SoC yields limited gains in speed due to BMS-imposed power tapering.
Unidirectional Flexibility: Flexibility is not a fixed power reserve but an energy-bounded capability. Short idle windows (5–10 minutes) allow for several percentage points of SoC variation, while windows of 20 minutes or more significantly expand the feasible energy shifting range.
Bidirectional Flexibility: The maximum extractable discharge energy is highly dependent on the activation timing and available idle time. The study finds that the extractable energy can exceed twice its value depending on when the flexibility maneuver is initiated.
Constraints: Battery-protection constraints (specifically the 60% depth-of-discharge limit) and idle time are the dominant limiting factors. For short idle times, feasibility is time-constrained; for longer idle times, it becomes battery-constrained.
Fleet Heterogeneity: There is significant variability across the fleet. Some ultra-fast charging vehicles exhibit limited bidirectional potential even with long idle times, while others can extract substantially more energy, highlighting the need for fleet-level aggregation rather than uniform modeling.

5. Significance and Claims

The paper claims that EV flexibility in fast and ultra-fast charging contexts should not be interpreted as a controllable power resource, but rather as a bounded, state-dependent, and time-coupled energy capability.

Operational Reality: The study asserts that the physically achievable flexibility is jointly determined by the EV-specific Power–SoC characteristics, the available connection time beyond the mandatory charging duration, and battery-protection limits.
Actionable Limits: The proposed framework provides actionable lower and upper bounds (LB/UB) for flexibility. These bounds define the feasible region for any admissible charging or discharging trajectory, offering system operators and aggregators realistic limits for scheduling, peak shaving, and short-duration flexibility services.
Strategic Implication: The findings suggest that strategies to extract value from EV flexibility should prioritize connection management (e.g., managing idle time) over simply increasing charger power ratings. Furthermore, energy services targeting fast-charging EVs should be designed around the mid-range SoC window (20–80%), where power levels are high and BMS limitations are less restrictive.
Methodological Shift: By shifting the perspective from "controllable power" to "realizable energy bounds," the framework offers a physically consistent basis for integrating EVs into system-level studies, avoiding the pitfalls of overestimating dispatchability inherent in simplified models.

The paper concludes that while the framework enables practical flexibility quantification without direct V2G measurements, future work is required to translate these bounds into real-time operational tools and to extend the analysis to heavy-duty and port-based electrification applications.